People with quadriplegia or other similar types of paralysis suffer from a partial or total loss of limb control. Often due to injury to the brain or spinal cord, quadriplegia can severely limit one's interactions with others, as well as professional opportunities, access to information, recreation, and self-care. Individuals with complete paralysis additionally lack the ability to effectively communicate, thus restricting a fundamental capability of human beings.
Recently, assistive technologies have enabled quadriplegics to perform certain activities, such as communication, which might be otherwise impossible. One such example, a brain-computer interface (BCI), which refers to a communication pathway between a user's brain and an external device, can allow the user to control a computer with mere thought through the interpretation of brain signals. A BCI-enabled device can utilize a user's brain signals, measured using brain sensors of various types (e.g., electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), etc.), as input to control operation of the device. As such, BCIs can restore communicative abilities for quadriplegics, among other possible opportunities, so as to significantly improve quality of life. Similar to the BCI, a muscle-computer interface (muCI) refers to a communication pathway between a user's muscle and an external device, allowing the user to control a computer with muscle activity, e.g., residual muscle activity after the user has suffered a debilitating accident, through the interpretation of muscle signals.
However, existing BCI-based technologies suffer from a variety of drawbacks including significant costs, safety concerns, and slow communication rates. For instance, BCIs with the fastest recorded typing rate require a brain implant, which in turn requires potentially dangerous and very expensive neurosurgery. These invasive BCIs allow for computer control of a traditional cursor, but rely on computer applications written for able-bodied users, which are not optimized for the fine-motor limitations of neurally controlled cursors. Meanwhile, state-of-the-art non-invasive BCIs can be safe and relatively inexpensive, but provide a dramatically slower typing speed due to reduced signal quality.
At the root of BCI applications is the measurement and interpretation of a user's brain signals. Some systems for interpreting brain signals rely on learning a correspondence between the raw electrical signal measured from neural activity in the brain and a thought or sequence of thoughts through a series of examples. For instance, a user whose brain is coupled to sensors might be asked to think about one of a small set of thought patterns, such as imagining moving his or her left arm, several dozen times for each pattern. From those examples, the system can train a mathematical model of the signals corresponding to each thought pattern.
Problematically, brain signals can be highly non-stationary, causing the nature of the signals to change frequently. This necessitates frequent retraining of the model with new examples, a process which takes considerable time. In order to limit retraining time, the number of thought patterns recognized by the system must be limited. Thus, a quadriplegic user attempting to operate a BCI-enabled device may encounter a time-consuming process of constant model retraining on one hand, or a limited suite of system-recognizable operations on the other.
Of course, most modern computer devices have installed thereon multiple different software applications such as a web browser, music player, instant messenger, word processor, and so on. Across the various applications, a user may need to perform one of potentially thousands of operations. Moreover, operations applicable to a first application may be inapplicable to a second application. Therefore, a challenge exists in mapping a fixed set of thoughts or thought sequences to a constantly changing suite of application-specific operations.